Val Piora Wheatear: Untitled

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library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8))

Settings used

All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.

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kable(gpr) %>% scroll_box(width = "100%")

gdl_id	keep	crop_start	crop_end	thr_dur	extent_N	extent_W	extent_S	extent_E	map_scale	map_max_sample	map_margin	prob_map_s	prob_map_s_calib	prob_map_thr	shift_k	kernel_adjust	calib_lon	calib_lat	calib_1_start	calib_1_end	calib_2_start	calib_2_end	calib_2_lon	calib_2_lat	prob_light_w	thr_prob_percentile	thr_gs	thr_as	RingNo	scientific_name	common_name	mass	wing_span	Color	sta_id_winter
16IQ	2	2016-07-01	2017-01-28 18:10:00	0	51	-18	5	16	4	300	30	1	1.3	0.9	0	1.4	8.68364	46.5427	2016-07-01	2016-09-13	NA	NA	NA	NA	0.09	0.95	120	100	N566640	Oenanthe oenanthe	Northern wheatear	NA	NA	#19D3F3	21

Pressure timeserie

The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationary period is illustrated by a different colored line.

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pressure_na <- pam$pressure %>%
  mutate(obs = ifelse(isoutlier | sta_id == 0, NA, obs))
p <- ggplot() +
  geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  # geom_point(data = subset(pam$pressure, isoutlier), aes(x = date, y = obs), colour = "black") +
  # geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = pressure0, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Pressure(hPa)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Pressure calibration

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pressure_ts_bind <- do.call("rbind", shortest_path_timeserie) %>%
  filter(!is.na(sta_id))

pam$pressure %>%
  left_join(pressure_ts_bind %>% dplyr::select(c("date", "pressure0")), by = "date") %>%
  mutate(diff = ifelse(is.na(pressure0), 0, obs - pressure0)) %>%
  filter(sta_id > 0 & !isoutlier) %>%
  group_by(sta_id) %>%
  mutate(sta_id = paste0(sta_id, " (SD=", round(sd(diff), 2), " ; N=", n(), ")")) %>%
  ggplot(aes(x = diff)) +
  geom_histogram(aes(y = (..count..) / tapply(..count.., ..PANEL.., sum)[..PANEL..]), binwidth = .2) +
  facet_wrap(~sta_id) +
  scale_x_continuous(name = "Pressure Geolocator - best match ERA5 (hPa)") +
  scale_y_continuous(name = "Normalized histogram")

Light

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raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
lightImage(tagdata = raw_geolight, offset = 0)
tsimagePoints(twl$twilight,
  offset = 0, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)

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hist(z, freq = F)
lines(fit_z, col = "red")

The probability map resulting from light data alone can be seen below.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
  i_s <- metadata(light_prob[[i_r]])$sta_id
  info <- pam$sta[pam$sta$sta_id == i_s, ]
  info_str <- paste0(i_s, " | ", info$start, "->", info$end)
  li_s <- append(li_s, info_str)
  l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
  addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Light vs Pressure

We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.

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 raw_geolight <- pam$light %>%
    transmute(
      Date = date,
      Light = obs
    )
 dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1], units = "days" )))
  long_id <- which(dur>5)

  par(mfrow = c(2, 3))
  for (i_s in long_id){
    twl_fl <- twl %>%
      filter(!deleted) %>%
      filter(twilight>shortest_path_timeserie[[i_s]]$date[1] & twilight<tail(shortest_path_timeserie[[i_s]]$date,1))
    sun <-  solar(twl_fl$twilight)
    z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
    hist(z_i, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(twl_fl),"twls"))
    lines(fit_z, col = "red")
    xlab("Zenith angle")
  }

Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.

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  lightImage(
    tagdata = raw_geolight,
    offset = gpr$shift_k / 60 / 60
  )
  tsimagePoints(twl$twilight,
                offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
                col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
  )
  for (ts in shortest_path_timeserie){
    twl_fl <- twl %>%
      filter(twilight>ts$date[1] & twilight<tail(ts$date,1))
    if (nrow(twl_fl)>0){
    tsimageDeploymentLines(twl_fl$twilight,
                           lon = ts$lon[1], ts$lat[1],
                           offset = gpr$shift_k / 60 / 60, lwd = 3,col = adjustcolor("orange", alpha.f = 0.5))
      
    }
  }

Stationay period information

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read_csv(paste0("../reports/figure_print/table_transition/transition_", params$gdl_id, ".csv")) %>% kable() %>% scroll_box(width = "100%")

…1	sta_id_s	sta_id_t	flight_duration	as_m	as_s	gs_m	gs_s	ws_m	ws_s	dist_m	dist_s	ws_m_support	ws_m_drift	start_flight	end_flight	sunrise	sunset	as_sp	gs_sp	ws_sp	dist_sp	ws_sp_support	ws_sp_drift	alt_min	alt_max	alt_mean	alt_med	alt_sumdabsdiff	alt_sumposdiff	gdl_id
1	1	2	3.0	37.08885	15.48453	36.44027	15.386209	7.118515	0.7572995	109.32080	46.15863	0.0409361	7.118397	2016-09-12 18:00:00	2016-09-12 21:00:00	2016-09-13 04:35:12	2016-09-12 18:13:03	32.824490	31.63740	7.114727	94.91220	-0.4093685	7.102940	2038.85795	4249.6979	3540.3050	4064.5223	3238.5651	2210.8399	16IQ
2	2	3	4.0	37.58059	13.14256	31.27040	12.953496	11.460933	0.9302699	125.08162	51.81399	-4.8465865	10.385740	2016-09-13 18:00:00	2016-09-13 22:00:00	2016-09-14 04:40:20	2016-09-13 18:14:25	31.051718	25.43909	10.757868	101.75635	-3.9571048	10.003651	2099.89492	3885.5487	3176.5380	3022.0178	2866.8032	972.3402	16IQ
3	3	4	0.5	32.85011	18.94894	31.92803	24.670824	10.757430	2.5719742	15.96402	12.33541	0.8768402	10.721634	2016-09-14 03:30:00	2016-09-14 04:00:00	2016-09-14 04:41:20	2016-09-13 18:16:34	32.433124	39.16539	10.355787	19.58270	7.5227494	7.116922	2579.22290	2881.6171	2730.4200	2730.4200	302.3942	0.0000	16IQ
4	4	5	2.5	34.33540	16.59299	39.04883	15.559596	15.424782	5.6813221	97.62207	38.89899	7.4754513	13.492276	2016-09-14 18:30:00	2016-09-14 21:00:00	2016-09-15 04:46:51	2016-09-14 18:15:36	28.720097	33.30968	14.322265	83.27421	7.3524917	12.290978	914.86577	2508.7662	1724.1304	1815.2973	2732.2431	1138.3426	16IQ
5	5	6	1.0	36.09962	16.37735	39.13690	19.225319	15.149421	11.4244919	39.13690	19.22532	5.8515042	13.973720	2016-09-15 01:30:00	2016-09-15 02:30:00	2016-09-15 04:47:51	2016-09-14 18:19:20	22.351863	19.66879	16.535138	19.66879	4.0842993	16.022774	518.29271	1412.8103	952.2536	925.6578	1381.6701	487.1525	16IQ
6	6	7	2.5	42.32883	18.58214	38.82818	17.108284	6.207005	3.3487909	97.07046	42.77071	-3.1623349	5.341025	2016-09-15 19:00:00	2016-09-15 21:30:00	2016-09-16 04:49:34	2016-09-15 18:18:23	26.190352	22.22587	4.388702	55.56467	-3.8847680	2.041883	315.83312	899.0733	538.3774	506.7137	2073.1680	1205.5361	16IQ
7	7	8	0.5	30.86716	23.28980	29.96778	25.062446	3.857361	2.9342593	14.98389	12.53122	-0.6646187	3.799673	2016-09-16 07:30:00	2016-09-16 08:00:00	2016-09-16 04:49:34	2016-09-15 18:17:54	3.196778	0.00000	3.196778	0.00000	NA	NA	233.26855	457.7046	345.4866	345.4866	224.4361	0.0000	16IQ
8	8	9	13.5	37.87521	6.73726	65.56797	5.209439	33.625773	2.7703386	885.16761	70.32743	30.4670068	14.228638	2016-09-17 18:00:00	2016-09-18 07:30:00	2016-09-18 05:00:23	2016-09-17 18:14:04	36.098523	63.92211	35.892599	862.94855	31.8450983	16.558031	70.48249	1476.0783	743.4512	759.0612	8829.4352	4327.2766	16IQ
9	9	10	2.0	33.93267	15.74667	35.31510	16.288515	7.034018	3.5031906	70.63020	32.57703	2.0558822	6.726868	2016-09-18 19:00:00	2016-09-18 21:00:00	2016-09-19 05:00:29	2016-09-18 18:09:58	26.038501	29.91094	4.835239	59.82189	4.0125869	2.697904	-93.84836	1189.0247	353.4827	317.5212	2478.8236	1195.9506	16IQ
10	10	11	2.5	32.82377	15.80523	34.24428	17.783013	6.665090	3.9558371	85.61069	44.45753	2.0396739	6.345325	2016-09-19 20:30:00	2016-09-19 23:00:00	2016-09-20 05:03:32	2016-09-19 18:07:10	22.587714	28.57697	6.696223	71.44242	6.1461671	2.657824	813.64783	1497.1830	1171.8246	1186.9080	1110.8499	213.6574	16IQ
11	11	12	1.0	34.64428	14.75161	34.71879	15.039239	6.535938	1.8787186	34.71879	15.03924	0.6896343	6.499453	2016-09-22 01:30:00	2016-09-22 02:30:00	2016-09-22 05:05:12	2016-09-21 18:05:56	27.628612	27.73859	6.075331	27.73859	0.7750759	6.025687	489.27037	668.0212	602.0915	648.9830	178.7508	0.0000	16IQ
12	12	13	1.5	31.47952	14.62411	30.82213	16.088285	6.569254	2.0233494	46.23319	24.13243	0.0356587	6.569157	2016-09-23 19:30:00	2016-09-23 21:00:00	2016-09-24 05:06:54	2016-09-23 18:02:53	32.851941	36.98249	6.213741	55.47373	4.4218882	4.365487	368.63401	1133.9144	650.3265	549.3787	1381.4182	765.2804	16IQ
13	13	14	1.0	35.68034	17.09562	32.99510	17.143920	5.668659	2.2742101	32.99510	17.14392	-2.3075666	5.177724	2016-09-24 03:00:00	2016-09-24 04:00:00	2016-09-24 05:07:54	2016-09-23 18:02:44	22.867140	22.78728	3.572653	22.78728	0.2000607	3.567047	603.09992	1475.3680	1114.6142	1265.3746	872.2680	872.2680	16IQ
14	14	15	10.5	60.58563	12.43562	63.81798	13.572737	7.819881	2.6613278	670.08881	142.51374	3.6295965	6.926512	2016-10-02 18:30:00	2016-10-03 05:00:00	2016-10-03 05:19:21	2016-10-02 17:50:53	41.797102	48.81643	10.822838	512.57247	7.7144069	7.590900	411.41539	3525.1762	2719.3652	3125.8606	6377.2713	2614.3111	16IQ
15	15	16	11.5	37.83741	11.17303	34.15254	14.311066	9.607799	10.6555008	392.75419	164.57726	-2.5322289	9.268097	2016-10-03 17:30:00	2016-10-04 05:00:00	2016-10-04 05:16:30	2016-10-03 17:54:58	25.287382	29.60238	7.566812	340.42741	4.9676076	5.707847	226.61581	1599.6662	789.7310	660.5537	5579.9595	2726.7820	16IQ
16	16	17	11.5	42.60696	16.22213	53.60666	18.279701	17.472061	5.5365263	616.47654	210.21656	12.7185067	11.979670	2016-10-04 18:00:00	2016-10-05 05:30:00	2016-10-05 05:18:24	2016-10-04 17:51:15	36.577595	29.22691	15.285042	336.10942	-4.2781797	14.674117	197.52266	2235.8945	1045.3244	907.4468	8188.2844	4301.7952	16IQ
17	17	18	10.0	38.91966	18.37944	51.71177	17.869762	19.011591	5.9124672	517.11768	178.69762	14.7046596	12.050460	2016-10-05 18:00:00	2016-10-06 04:00:00	2016-10-06 05:26:57	2016-10-05 17:52:48	30.712738	45.11269	19.989959	451.12687	16.5306127	11.239987	395.29340	3467.0079	1823.4529	1520.8623	8445.9436	4089.2364	16IQ
18	18	19	8.0	29.61892	15.67130	35.89684	22.724972	17.874172	7.4241546	287.17476	181.79977	10.1790183	14.692638	2016-10-06 19:00:00	2016-10-07 03:00:00	2016-10-07 05:30:13	2016-10-06 18:01:43	27.945055	50.17790	25.813715	401.42320	23.9472297	9.637326	286.58205	1636.4986	692.5006	503.8918	4113.5428	2044.4760	16IQ
19	19	20	1.0	32.52622	14.54554	35.14452	18.533317	18.759959	2.9770665	35.14452	18.53332	7.5277459	17.183396	2016-10-07 21:00:00	2016-10-07 22:00:00	2016-10-08 05:31:20	2016-10-07 18:05:55	26.397970	38.34644	15.789102	38.34644	13.3375169	8.450230	246.69684	813.6248	436.6578	249.6518	1130.9010	566.9280	16IQ
20	20	21	2.0	35.52607	17.77283	33.86055	18.459284	17.415097	3.9381675	67.72109	36.91857	2.7719700	17.193074	2016-10-08 23:30:00	2016-10-09 01:30:00	2016-10-09 05:30:26	2016-10-08 18:06:15	33.040540	19.18546	18.927401	38.37092	-9.5215077	16.358099	267.34748	490.2704	363.1242	296.9089	792.1669	388.1909	16IQ

16IQ

Settings used

Pressure timeserie

Pressure calibration

Light

Light vs Pressure

Stationay period information